System and method of predicting future fractures

ABSTRACT

Methods of predicting fracture risk of a patient include: obtaining an image of a bone of the patient; determining one or more bone structure parameters; predicting a fracture line with the bone structure parameter; predicting a fracture load at which a fracture will happen; estimating body habitus of the patient; calculating a peak impact force on the bone when the patient falls; and predicting a fracture risk by calculating the ratio between the peak impact force and the fracture load. Inventive methods also include determining the effect of a candidate agent on any subject&#39;s risk of fracture.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 11/228,126, filed Sep. 16, 2005, now U.S. Pat. No. 8,600,124,which claims the benefit of U.S. Patent Application Ser. No. 60/610,447,filed Sep. 16, 2004, the disclosures of which are incorporated byreference herein in its entireties.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to using imaging methods for predicting fracturerisk and/or location based on radiographs.

2. Description of the Related Art

Osteoporosis is among the most common conditions to affect themusculoskeletal system, as well as a frequent cause of locomotor painand disability. Osteoporosis can occur in both human and animal subjects(e.g. horses). Osteoporosis (OP) occurs in a substantial portion of thehuman population over the age of fifty. The National OsteoporosisFoundation estimates that as many as 44 million Americans are affectedby osteoporosis and low bone mass. In 1997 the estimated cost forosteoporosis related fractures was $13 billion. That figure increased to$17 billion in 2002 and is projected to increase to $210-240 billion by2040. Currently it is expected that one in two women over the age of 50will suffer an osteoporosis-related fracture.

Imaging techniques are important diagnostic tools, particularly for bonerelated conditions such as osteoporosis. Currently available techniquesfor the noninvasive assessment of the skeleton for the diagnosis ofosteoporosis or the evaluation of an increased risk of fracture includedual x-ray absorptiometry (DXA) (Eastell et al. (1998) New Engl J. Med338:736-746); quantitative computed tomography (QCT) (Cann (1988)Radiology 166:509-522); peripheral DXA (pDXA) (Patel et al. (1999) JClin Densitom 2:397-401); peripheral QCT (pQCT) (Gluer et al. (1997)Semin Nucl Med 27:229-247); x-ray image absorptiometry (RA) (Gluer etal. (1997) Semin Nucl Med 27:229-247; and U.S. Pat. No. 6,246,745); andquantitative ultrasound (QUS) (Njeh et al. “Quantitative Ultrasound:Assessment of Osteoporosis and Bone Status”, 1999, Martin-Dunitz, LondonEngland; WO 9945845; WO 99/08597; and U.S. Pat. No. 6,077,224 which isincorporated herein by reference in its entirety).

DXA of the spine and hip has established itself as the most widely usedmethod of measuring bone mineral density (BMD). Tothill, P. and D. W.Pye, (1992) Br J Radiol 65:807-813. The fundamental principle behind DXAis the measurement of the transmission through the body of x-rays of 2different photon energy levels. Because of the dependence of theattenuation coefficient on the atomic number and photon energy,measurement of the transmission factors at 2 energy levels enables thearea densities (i.e., the mass per unit projected area) of 2 differenttypes of tissue to be inferred. In DXA scans, these are taken to be bonemineral (hydroxyapatite) and soft tissue, respectively. However, it iswidely recognized that the accuracy of DXA scans is limited by thevariable composition of soft tissue. Because of its higher hydrogencontent, the attenuation coefficient of fat is different from that oflean tissue. Differences in the soft tissue composition in the path ofthe x-ray beam through bone compared with the adjacent soft tissuereference area cause errors in the BMD measurements, according to theresults of several studies. Tothill, P. and D. W. Pye, (1992) Br JRadiol, 65:807-813; Svendsen, O. L., et al., (1995) J Bone Min Res10:868-873. Moreover, DXA systems are large and expensive, ranging inprice between $75,000 and $150,000.

Quantitative computed tomography (QCT) is usually applied to measure thetrabecular bone in the vertebral bodies. Cann (1988) Radiology166:509-522. QCT studies are generally performed using a single kVsetting (single-energy QCT), when the principal source of error is thevariable composition of the bone marrow. However, a dual-kV scan(dual-energy QCT) is also possible. This reduces the accuracy errors butat the price of poorer precision and higher radiation dose. Like DXA,however, QCT are very expensive and the use of such equipment iscurrently limited to few research centers.

Quantitative ultrasound (QUS) is a technique for measuring theperipheral skeleton. Njeh et al. (1997) Osteoporosis Int 7:7-22; andNjeh et al., Quantitative Ultrasound: Assessment of Osteoporosis andBone Status, 1999, Martin Dunitz, London, England. There is a widevariety of equipment available, with most devices using the heel as themeasurement site. A sonographic pulse passing through bone is stronglyattenuated as the signal is scattered and absorbed by trabeculae.Attenuation increases linearly with frequency, and the slope of therelationship is referred to as broadband ultrasonic attenuation (BUA;units: dB/MHz). BUA is reduced in patients with osteoporosis becausethere are fewer trabeculae in the calcaneus to attenuate the signal. Inaddition to BUA, most QUS systems also measure the speed of sound (SOS)in the heel by dividing the distance between the sonographic transducersby the propagation time (units: m/s). SOS values are reduced in patientswith osteoporosis because with the loss of mineralized bone, the elasticmodulus of the bone is decreased. There remain, however, severallimitations to QUS measurements. The success of QUS in predictingfracture risk in younger patients remains uncertain. Another difficultywith QUS measurements is that they are not readily encompassed withinthe WHO definitions of osteoporosis and osteopenia. Moreover, nointervention thresholds have been developed. Thus, measurements cannotbe used for therapeutic decision-making.

There are also several technical limitations to QUS. Many devices use afoot support that positions the patient's heel between fixedtransducers. Thus, the measurement site is not readily adapted todifferent sizes and shapes of the calcaneus, and the exact anatomic siteof the measurement varies from patient to patient. It is generallyagreed that the relatively poor precision of QUS measurements makes mostdevices unsuitable for monitoring patients' response to treatment. Gluer(1997) J Bone Min Res 12:1280-1288.

Radiographic absorptiometry (RA) is a technique that was developed manyyears ago for assessing bone density in the hand, but the technique hasrecently attracted renewed interest. Gluer et al. (1997) Semin Nucl Med27:229-247. With this technique, BMD is measured in the phalanges.

Furthermore, current methods and devices do not generally take intoaccount bone structure analyses. See, e.g., Ruttimann et al. (1992) OralSurg Oral Med Oral Pathol 74:98-110; Southard & Southard (1992) OralSurg Oral Med Oral Pathol 73:751-9; White & Rudolph, (1999) Oral SurgOral Med Oral Pathol Oral Radiol Endod88:628-35.

BMD does not accurately predict the presence of osteoporotic fracture.See, e.g., Riggs et al. (1982) J Clin Invest 70:716-723; Krolner, B. andS. P. Nielsen (1982) Clin Sci. 62:329-336; Ott et al. (1987) J BoneMiner Res, 2:201-210; and Pacifici et al. (1987) J Clin EndocrinolMetab, 64:209-214. While BMD is correlated with long-term fracture riskin population based studies (Kains (1994) Osteoporosis Int 4:368-381),it cannot take into account factors that vary from patient to patientand that are major determinants of individual failure load and resultantfracture (Hayes, W. C. and M. L. Bouxsein, Biomechanics of cortical andtrabecular bone: Implications for assessment of fracture risk, in BasicOrthopaedic Biomechanics, V. C. Mow and W. C. Hayes, Editors, 1997,Lippincott-Raven Publishers: Philadelphia, p. 69-111; Kroonenberg et al.(1995) J Biomech Eng. 117(3):309-318; Kroonenberg et al. (1996)Biomechanics 29(6):807-811; and Robinovitch et al. (1991) J Biomech Eng.113:366-374). These factors include bone architecture and structure,bone morphology, and biomechanical loading and impact load. Indeed,patients receiving osteoclast inhibiting, anti-resorptive drugs showremarkable reductions in incident osteoporotic fractures by 60-65% butonly small changes in BMD on the order of 4.0-4.5% (Reginster et al.(2000) Osteoporosis Int.,11(1):83-91; and Harris et al. (1999) JAMA14:1344-1352), strongly indicating a significant discrepancy betweenclinical outcomes and BMD measurements of bone health.

Thus, there remains a need for compositions and methods for predictingfracture risk.

SUMMARY OF THE INVENTION

The invention discloses a method for predicting a fracture by analyzingat least one bone structure parameter. The method comprises: obtainingan image of a part of skeleton of a patient; locating at least oneregion of interest on the image of the patient; extracting image datafrom the image of the patient; deriving at least one bone structureparameter from the image data of the patient; and predicting a fracturewith the bone structure parameter of the patient. The bone structureparameter includes, but not limited to, hone micro-structure parametersand bone macro-structure parameters.

In certain aspects, described herein are methods of diagnosing,monitoring and/or predicting bone or articular disease (e.g., the riskof fracture) in a subject, the method comprises the steps of:determining one or more micro-structural parameters, and/or one or moremacro-structure parameters, possibly with other bone parameters, of abone or a joint in the subject; and combining at least two of theparameters to predict the risk of bone or articular disease. Themicro-structural and macro-structure parameters may be, for example, oneor more of the measurements/parameters shown in Tables 1 and 2. Incertain embodiments, one or more micro-structural parameters and one ormore macro-structural parameters are combined. In other embodiments, oneor more micro-structural parameters and one or more other boneparameters are combined. In further embodiments, one or moremacro-structure parameters and one or more other parameters arecombined. In still further embodiments, one or more macro-structuralparameters, one or more micro-structural parameters and one or moreother bone parameters are combined.

In any of the methods described herein, the comparing may compriseunivariate, bivariate and/or multivariate statistical analysis of one ormore of the parameters, including at least one bone structure parameter.In certain embodiments, the methods may further comprise comparing theparameters to data derived from a reference database of known diseaseparameters.

In any of the methods described herein, the parameters are determinedfrom an image obtained from the subject. In certain embodiments, theimage comprises one or more regions of bone (e.g., patella, femur,tibia, fibula, pelvis, spine, etc). The image may be automatically ormanually divided into two or more regions of interest. Furthermore, inany of the methods described herein, the image may be, for example, anx-ray image, a CT scan, an MRI or the like and optionally includes oneor more calibration phantoms.

In any of the methods described herein, the predicting includesperforming univariate, bivariate or multivariate statistical analysis ofthe analyzed data and referencing the statistical analysis values to afracture risk model. Fracture risk models can comprise, for example,data derived from a reference database of known fracture loads withtheir corresponding values of macro-anatomical, micro-anatomicalparameters, and/or clinical risk factors.

In another aspect, the invention includes a method of determining theeffect of a candidate agent on a subject's prognosis for musculoskeletaldisease comprising: predicting a first risk of musculoskeletal diseasein the subject according to any of the predictive methods describedherein; administering a candidate agent to the subject; predicting asecond risk of the musculoskeletal disease in the subject according toany of the predictive methods described herein; and comparing the firstand second risks, thereby determining the effect of the candidate on thesubject's prognosis for the disease. In any of these methods, thecandidate agent can be administered to the subject in any modality, forexample, by injection (intramuscular, subcutaneous, intravenous), byoral administration (e.g., ingestion), topical administration, mucosaladministration or the like. Furthermore, the candidate agent may be asmall molecule, a pharmaceutical, a biopharmaceutical, anagropharmaceuticals and/or combinations thereof. It is important to notethat an effect on a subject's prognosis for musculoskeletal disease canoccur in agents intended to have an effect, such as a therapeuticeffect, on musculoskeletal disease as well as agents intended toprimarily effect other tissues in the body but which have a secondary,or tangential, effect on musculoskeletal disease. Further, the agent canbe evaluated for the ability to effect diseases such as the risk of bonefracture (e.g., osteoporotic fracture).

In other aspects, the invention includes a kit that is provided foraiding in the prediction of musculoskeletal disease (e.g., fracturerisk). The kit typically comprises a software program that usesinformation obtained from an image to predict the risk or disease (e.g.,fracture). The kit can also include a database of measurements forcomparison purposes. Additionally, the kit can include a subset of adatabase of measurements for comparisons.

In any of these methods, systems or kits, additional steps can beprovided. Such additional steps include, for example, enhancing imagedata.

Suitable subjects for these steps include for example mammals, humansand horses. Suitable anatomical regions of subjects include, forexample, dental, spine, hip, knee and bone core x-rays.

A variety of systems can be employed to practice the inventions.Typically at least one of the steps of any of the methods is performedon a first computer. Although, it is possible to have an arrangementwhere at least one of the steps of the method is performed on a firstcomputer and at least one of the steps of the method is performed on asecond computer. In this scenario the first computer and the secondcomputer are typically connected. Suitable connections include, forexample, a peer to peer network, direct link, intranet, and internet.

It is important to note that any or all of the steps of the inventionsdisclosed can be repeated one or more times in series or in parallelwith or without the repetition of other steps in the various methods.This includes, for example repeating the step of locating a region ofinterest, or obtaining image data.

Data can also be converted from 2D to 3D to 4D and back; or from 2D to4D. Data conversion can occur at multiple points of processing theinformation. For example, data conversion can occur before or afterpattern evaluation and/or analysis.

Any data obtained, extracted or generated under any of the methods canbe compared to a database, a subset of a database, or data previouslyobtained, extracted or generated from the subject. For example, knownfracture load can be determined for a variety of subjects and some orall of this database can be used to predict fracture risk by correlatingone or more micro-structural parameters or macro-structural parameters(Tables 1 and 2) with data from a reference database of fracture loadfor age, sex, race, height and weight matched individuals.

In any of the methods described herein, the analysis can comprise usingone or more computer programs (or units). Additionally, the analysis cancomprise identifying one or more regions of interest (ROI) in the image,either prior to, concurrently or after analyzing the image, e.g. forinformation on bone structure. Bone structural information can be, forexample, one or more of the parameters shown in Table 1 and Table 2. Thevarious analyses can be performed concurrently or in series. Further,when using two or more indices each of the indices can be weightedequally or differently, or combinations thereof where more than twoindices are employed. Additionally, any of these methods can alsoinclude analyzing the image for bone structure information using any ofthe methods described herein.

These and other embodiments of the subject invention will readily occurto those of skill in the art in light of the disclosure herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart of a method for collecting quantitative and/orqualitative data according to one embodiment of the present invention.

FIG. 2 depicts exemplary regions of interest (ROIs), as analyzed inExample 1 of the present invention.

FIGS. 3A and 3B are graphs depicting correlation of 2D and 3Dmeasurements according to one embodiment of the present invention. FIG.3A depicts correlation of 2D and 3D trabecular spacing. FIG. 3B depictscorrelation of 2D trabecular perimeter/trabecular area with 3D bonesurface/bone volume.

FIGS. 4A and 4B are graphs depicting correlation of 2D measurements withfracture load measurements according to one embodiment of the presentinvention. FIG. 4A depicts 2D trabecular perimeter/trabecular area v.fracture load. FIG. 4B depicts 2D trabecular separation vs. fractureload.

FIGS. 5A and 5B are graphs depicting correlation of femoral neck DXAbone mineral density (BMD) and fracture load (FIG. 5A) and correlationof predicted fracture load and actual fracture load (FIG. 5B) accordingto a conventional method.

FIGS. 6A to 6D depict sliding window analysis maps for two differentfemora. The top maps (FIGS. 6A and 6B) depict area ratio analysis. Thebottom maps (FIGS. 6C and 6D) depict trabecular perimeter analysis.Black lines show fracture lines from post-fracture x-rays for proximaland distal fragments. Red lines show results of watershed analysis ofparameter maps. Color scale ranges from blue (low values) to red (highvalues).

FIGS. 7A and 7B illustrate exemplary steps for predicting fracture riskvia individualized fracture risk index (IFRI) according to oneembodiment of the present invention.

FIG. 8 shows an exemplary bone structure parameter map of a proximalfemur. Color scale ranges from blue (low values) to red (high values).Regions are separated along low values (“valleys”) using watershedsegmentation.

FIG. 9 is an image of a femur and shows an approximation of the femoralaxes by two linear segments in the neck (solid blue line) and shaft(solid green line). Also shown is a hyberbolic curve fitted to theintertrochanteric region. White arrows show loading simulating sideimpact fall. Three examples of cross-sectional lines are also shown.

FIG. 10 depicts trochanteric and femoral neck fracture paths (red) thatmay be constructed by calculating the distance of segments (yellow) tocross-sectional lines (black).

FIG. 11 depicts definition of a region of interest (ROI) along thepredicted fracture path using a region growing technique. This region ofinterest is used for a structural analysis of the trabecular bone.Contact points between the trabecular ROI and the cortical bonedetermine the area for cortical bone measurements.

FIG. 12 is a block diagram of a computer program for predicting fracturerisk according to one embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

The following description is presented to enable any person skilled inthe art to make and use the invention. Various modifications to theembodiments described will be readily apparent to those skilled in theart, and the generic principles defined herein can be applied to otherembodiments and applications without departing from the spirit and scopeof the present invention as defined by the appended claims. Thus, thepresent invention is not intended to be limited to the embodimentsshown, but is to be accorded the widest scope consistent with theprinciples and features disclosed herein. To the extent necessary toachieve a complete understanding of the invention disclosed, thespecification and drawings of all issued patents, patent publications,and patent applications cited in this application are incorporatedherein by reference.

The practice of the present invention employs, unless otherwiseindicated, methods of imaging and image processing within the skill ofthe art. Currently available imaging methods are explained fully in theliterature. See, e.g., WO 02/22014; X-Ray Structure Determination: APractical Guide, 2^(nd) Edition, editors Stout and Jensen, 1989, JohnWiley & Sons, publisher; Body CT: A Practical Approach, editor Slone,1999, McGraw-Hill publisher; The Essential Physics of Medical Imaging,editors Bushberg, Seibert, Leidholdt Jr & Boone, 2002, Lippincott,Williams & Wilkins; X-ray Diagnosis: A Physician's Approach, editor Lam,1998 Springer-Verlag, publisher; Dental Radiology: Understanding theX-Ray Image, editor Laetitia Brocklebank 1997, Oxford University Presspublisher; Digital Image Processing, editor Kenneth R. Castleman, 1996,Prentice Hall, publisher; The Image Processing Handbook, editor John C.Russ, 3^(th) Edition, 1998, CRC Press; and Active Contours: TheApplication of Techniques from Graphics, Vision, Control Theory andStatistics to Visual Tracking of Shapes in Motion, Editors Andrew Blake,Michael Isard, 1999 Springer Verlag. As will be appreciated by those ofskill in the art, as the field of imaging continues to advance, methodsof imaging currently employed can evolve over time. Thus, any imagingmethod or technique that is currently employed is appropriate forapplication of the teachings of this invention as well as techniquesthat can be developed in the future. A further detailed description ofimaging methods is not provided in order to avoid obscuring theinvention.

FIG. 1 shows a method for collecting quantitative and/or qualitativedata according to one embodiment of the present application. Step 101 isused to locate a part of the body of a subject, for example in a humanbody, for study. The part of the body located for study is the region ofanatomical interest (RAI). In locating a part of the body for study, adetermination is made to, for example, take an image or a series ofimages of the body at a particular location, e.g. hip, dental, spine,etc. Images include, for example, conventional x-ray images, x-raytomosynthesis, ultrasound (including A-scan, B-scan and C-scan),computed tomography (CT scan), magnetic resonance imaging (MRI), opticalcoherence tomography, single photon emission tomography (SPECT), andpositron emission tomography, or such other imaging tools that a personof skill in the art would find useful in practicing the invention. Oncethe image is taken, one or more regions of interest (ROI) can bemanually and/or automatically located within the image at step 103. Askilled artisan would appreciate that algorithms can be used toautomatically place regions of interest in a particular image. Forinstance, Example 1 below describes automatic placement of ROIs infemurs. Image data is extracted from the image at step 105. Finally,quantitative and/or qualitative data is extracted from the image data atstep 107. The quantitative and/or qualitative data extracted from theimage include at least one measurement about bone structure, such asthose shown in Tables 1 and 2.

Each step of locating a part of the body for study 101, optionallylocating a region of interest 103, obtaining image data 105, andderiving quantitative and/or qualitative data 107, can be repeated oneor more times at step 102,104, 106, or 108, respectively, as desired.Image data can be optionally enhanced by applying image processingtechniques, such as noise filtering or diffusion filtering, tofacilitate further analysis.

TABLE 1 Representative Parameters Measured with Quantitative andQualitative Image Analysis Methods for Micro-structure PARAMETERMEASUREMENTS Measurements on Trabecular contrast extracted micro-Standard deviation of background subtracted ROI structures Coefficientof Variation of ROI (Standard deviation/mean) (Trabecular equivalentthickness/Marrow equivalent thickness) Hough transform Trabecular area(Pixel count of extracted trabeculae) Trabecular area/Total areaTrabecular perimeter (Count of trabecular pixels with marrow pixels intheir neighborhood, proximity or vicinity) Trabecular distance transform(For each trabecular pixel, calculation of distance to closest marrowpixel) Marrow distance transform (For each marrow pixel, calculation ofdistance to closest trabecular pixel) Trabecular distance transformregional maximal values (mean, min., max, std. Dev). (Describesthickness and thickness variation of trabeculae) Marrow distancetransform regional maximal values (mean, min., max, std. Dev) Starvolume (Mean volume of all the parts of an object which can be seenunobscured from a random point inside the object in all possibledirections) Trabecular Bone Pattern Factor (TBPf = (P1 − P2)/(A1 − A2)where P1 and A1 are the perimeter length and trabecular bone area beforedilation and P2 and A2 corresponding values after a single pixeldilation, measure of connectivity) Measurements on Connected skeletoncount or Trees (T) skeleton of Node count (N) extracted micro- Segmentcount (S) structures Node-to-node segment count (NN) Node-to-free-endsegment count (NF) Node-to-node segment length (NNL) Node-to-free-endsegment length (NFL) Free-end-to-free-end segment length (FFL)Node-to-node total struts length (NN.TSL) Free-end-to-free-ends totalstruts length(FF.TSL) Total struts length (TSL) FF.TSL/TSL NN.TSL/TSLLoop count (Lo) Loop area Mean distance transform values for eachconnected skeleton Mean distance transform values for each segment(Tb.Th) Mean distance transform values for each node-to-node segment(Tb.Th.NN) Mean distance transform values for each node-to-free-endsegment (Tb.Th.NF) Orientation (angle) of each segment Angle betweensegments Length-thickness ratios (NNL/Tb.Th.NN) and (NFL/Tb.Th.NF)Interconnectivity index (ICI) ICI = (N * NN)/(T * (NF + 1)) Measurementson Standard deviation of background subtracted ROI gray level images ofCoefficient of Variation of ROI (Standard deviation/mean)micro-structures Fractal dimension Fourier spectral analysis (Meantransform coefficient absolute value and mean spatial first moment)Predominant orientation of spatial energy spectrum Watershedsegmentation is applied to gray level images. Statistics of watershedsegments are: Total area of segments Number of segments normalized bytotal area of segments Average area of segments Standard deviation ofsegment area Smallest segment area Largest segment area

All micro-structural measurements can be applied in adirection-sensitive fashion or only on selected structures. For example,they can be applied to selected structures that are oriented parallel orperpendicular to stress lines. The techniques can also be used tomeasure only horizontal or vertical structures.

As will be appreciated by those of skill in the art, the parameters andmeasurements shown in the tables are provided for illustration purposesonly. It will be apparent that the terms micro-structural parameters,micro-architecture, micro-anatomic structure, micro-structural andtrabecular architecture may be used interchangeably. Furthermore, theterms macro-structural parameters, macro-structure, macro-anatomicparameters, macro-anatomic structure, macro-anatomy, macro-architectureand bone geometry may be used interchangeably. In addition, otherparameters and measurements, ratios, derived values or indices can beused to extract quantitative and/or qualitative information about theROI without departing from the scope of the invention. Additionally,where multiple ROI or multiple derivatives of data are used, theparameter measured can be the same parameter or a different parameterwithout departing from the scope of the invention. Additionally, datafrom different ROIs can be combined or compared as desired. Additionalmeasurements can be performed that are selected based on the anatomicalstructure to be studied as described below. For instance, biomechanicalaspects of the joint can also be evaluated. For example, the product ofthe average trabecular-computed tomography number and the totalcross-sectional area of the sub-capital, basicervical orintertrochanteric regions can be determined, as it has been shown tocorrelate highly with failure loads. See, e.g., Lotz et al. (1990) J.Bone Joint Surg. Am. 72:689-700; Courtney et al. (1995) J. Bone JointSurg. Am. 77(3):387-395; Pinilla et al. (1996) Calcif Tissue Int.58:231-235.

Once the quantitative and/or qualitative data is extracted from theimage, it can be manipulated to assess the severity of the disease andto determine disease staging (e.g., mild, moderate, severe or anumerical value or index). The information can also be used to monitorprogression of the disease and/or the efficacy of any interventionalsteps that have been taken. Finally, the information can be used topredict the progression of the disease or to randomize patient groups inclinical trials.

After an image of an RAI is taken, one or more regions of interest canbe identified within the image at step 103. The ROI can take up theentire image, or nearly the entire image. Alternatively, more than oneROI can be identified in an image, as shown in FIG. 2. One or more ofthe ROI may overlap or abut. As will be appreciated by a person of skillin the art, the number of ROI identified in an image is not limited tothe seven depicted in FIG. 2. As also will be appreciated by those ofskill in the art, where multiple ROI are used, any or all of the ROI canbe organized such that it does not overlap, it abuts withoutoverlapping, it overlaps partially, it overlaps completely (for examplewhere a first ROI is located completely within a second identified ROI),and combinations thereof. Further the number of ROI per image can rangefrom one (ROI₁) to n (ROI_(n)) where n is the number of ROI to beanalyzed.

Bone structure analyses, possibly together with bone density, and/orbiomechanical (e.g. derived using finite element modeling) analyses, canbe applied within a region of predefined size and shape and position.This region of interest can also be referred to as a “window”.Processing can be applied repeatedly by moving the window to differentpositions of the image. For example, a field of sampling points can begenerated and the analysis performed at these points. The results of theanalyses for each parameter can be stored in a matrix space, e.g., whereits position corresponds to the position of the sampling point where theanalysis occurred, thereby forming a map of the spatial distribution ofthe parameter (a parameter map). The sampling field can have regularintervals or irregular intervals with varying density across the image.The window can have variable size and shape, for example to account fordifferent patient size or anatomy.

In another embodiment, rather than a fixed ROI (e.g., FIG. 3), the imagemay be overlaid with a regular grid, for example, a region of interestof a fixed size (e.g., of any shape) may be placed at each grid node,and parameters are evaluated within the boundaries of the ROI at eachposition. This results in a value for each bone parameter at each gridnode, which can be displayed in a color-coded map of the proximal femurfor each parameter.

The amount of overlap between the windows can be determined, forexample, using the interval or density of the sampling points (andresolution of the parameter maps). Thus, the density of sampling pointsis set higher in regions where higher resolution is desired and setlower where moderate resolution is sufficient, in order to improveprocessing efficiency. The size and shape of the window would determinethe local specificity of the parameter. Window size is preferably setsuch that it encloses most of the structure being measured. Oversizedwindows are generally avoided to help ensure that local specificity isnot lost.

The shape of the window can be varied to have the same orientationand/or geometry of the local structure being measured to minimize theamount of structure clipping and to maximize local specificity. Thus,both 2D and/or 3D windows can be used, as well as combinations thereof,depending on the nature of the image and data to be acquired.

In another embodiment, bone structure analysis, possibly together withbone density and/or biomechanical (e.g. derived using finite elementmodeling) analyses, can be applied within a region of predefined sizeand shape and position. The region is generally selected to includemost, or all, of the anatomic region under investigation and,preferably, the parameters can be assessed on a pixel-by-pixel basis(e.g., in the case of 2D or 3D images) or a voxel-by-voxel basis in thecase of cross-sectional or volumetric images (e.g., 3D images obtainedusing MR and/or CT). Alternatively, the analysis can be applied toclusters of pixels or voxels wherein the size of the clusters istypically selected to represent a compromise between spatial resolutionand processing speed. Each type of analysis can yield a parameter map.

Parameter maps can be based on measurement of one or more parameters inthe image or window; however, parameter maps can also be derived usingstatistical methods. In one embodiment, such statistical comparisons caninclude comparison of data to a reference population, e.g. using az-score or a T-score. Thus, parameter maps can include a display ofz-scores or T-scores.

Additional measurements relating to the site to be measured can also betaken. For example, measurements can be directed to dental, spine, hip,knee or bone cores. Examples of suitable site specific measurements areshown in Table 2.

TABLE 2 Common and site specific measurements of bone macro-structureparameters Measurements on The following parameters are derived from theextracted macro-structures macro-structures: common to dental,Calibrated density of extracted structures spine, hip, knee or boneCalibrated density of background cores images Average intensity ofextracted structures Average intensity of background (area other thanextracted structures) Structural contrast (average intensity ofextracted structures/ average intensity of background) Calibratedstructural contrast (calibrated density extracted structures/calibrateddensity of background) Total area of extracted structures Bone patternfactor; measures concavity and convexity of structures Average length ofstructures (units of connected segments) Maximum length of structuresAverage thickness of structures Maximum thickness of structures Regionalmaximum thickness of structures Standard deviation of thickness alongstructures Average orientation angle of structure segmentss Structuresegment tortuosity; a measure of straightness Structure segmentsolidity; another measure of straightness Parameters specific to Shaftangle hip images Neck angle Average and minimum diameter of femur neckHip axis length CCD (caput-collum-diaphysis) angle Width of trochantericregion Largest cross-section of femur head Standard deviation ofcortical bone thickness within ROI Minimum, maximum, mean and medianthickness of cortical bone within ROI Hip joint space width Parametersspecific to Superior endplate cortical thickness (anterior, center,spine images posterior) Inferior endplate cortical thickness (anterior,center, posterior) Anterior vertebral wall cortical thickness (superior,center, inferior) Posterior vertebral wall cortical thickness (superior,center, inferior) Superior aspect of pedicle cortical thickness inferioraspect of pedicle cortical thickness Vertebral height (anterior, center,posterior) Vertebral diameter (superior, center, inferior), Pediclethickness (supero-inferior direction). Maximum vertebral height Minimumvertebral height Average vertebral height Anterior vertebral heightMedial vertebral height Posterior vertebral height Maximuminter-vertebral height Minimum inter-vertebral height Averageinter-vertebral height Parameters specific to Average medial joint spacewidth knee images Minimum medial joint space width Maximum medial jointspace width Average lateral joint space width Minimum lateral jointspace width Maximum lateral joint space width

As will be appreciated by those of skill in the art, measurement andimage processing techniques are adaptable to be applicable to bothmicro-architecture and macro-anatomical structures. Examples of thesemeasurements are shown in Table 2.

As noted above, analysis can also include one or more additionaltechniques, for example, Hough transform, mean pixel intensity analysis,variance of pixel intensity analysis, soft tissue analysis and the like.See, e.g., co-owned International Application WO 02/30283.

Calibrated density typically refers to the measurement of intensityvalues of features in images converted to its actual material density orexpressed as the density of a reference material whose density is known.The reference material can be metal, polymer, plastics, bone, cartilage,etc., and can be part of the object being imaged or a calibrationphantom placed in the imaging field of view during image acquisition.

Extracted structures typically refer to simplified or amplifiedrepresentations of features derived from images. Bone structureparameters include, for example, micro-structure parameters andmacro-structure parameters. Micro-structure parameters could be, forexample, the measurements in Table 1. Macro-structure parameters couldbe; for example, the parameters in Table 2. An example would be binaryimages of trabecular patterns generated by background subtraction andthresholding. Another example would be binary images of cortical bonegenerated by applying an edge filter and thresholding. The binary imagescan be superimposed on gray level images to generate gray level patternsof structure of interest.

Distance transform typically refers to an operation applied on binaryimages where maps representing distances of each 0 pixel to the nearest1 pixel are generated. Distances can be calculated by the Euclidianmagnitude, city-block distance, La Place distance or chessboarddistance.

Distance transform of extracted structures typically refers to distancetransform operation applied to the binary images of extractedstructures, such as those discussed above with respect to calibrateddensity.

Skeleton of extracted structures typically refers to a binary image of 1pixel wide patterns, representing the centerline of extractedstructures. It is generated by applying a skeletonization or medialtransform operation, by mathematical morphology or other methods, on animage of extracted structures.

Skeleton segments typically are derived from skeleton of extractedstructures by performing pixel neighborhood analysis on each skeletonpixel. This analysis classifies each skeleton pixel as a node pixel or askeleton segment pixel. A node pixel has more than 2 pixels in its8-neighborhood. A skeleton segment is a chain of skeleton segment pixelscontinuously 8-connected. Two skeleton segments are separated by atleast one node pixel.

Watershed segmentation as it is commonly known to a person of skill inthe art, typically is applied to gray level images to characterize graylevel continuity of a structure of interest. The statistics ofdimensions of segments generated by the process are, for example, thoselisted in Table 1 above. As will be appreciated by those of skill in theart, however, other processes can be used without departing from thescope of the invention. As described in the Examples, watershedtransformation may be applied as follows. The image (or its negative,depending on whether peaks or valleys are to be located) is consideredas a topographic relief, in which higher intensities correspond tohigher topographic heights. This relief can be divided (segmented) intocatchment basins, one for each local minimum of the image, where acatchment basin is defined as the area in which a raindrop would flow tothe corresponding minimum. The lines that separate catchment basins fromeach other are the watersheds.

At step 109, the extracted image data obtained at step 107 can beconverted to a 2D pattern, a 3D pattern or a 4D pattern, for exampleincluding velocity or time, to facilitate data analyses. Followingconversion to 2D, 3D or 4D pattern the images are evaluated for patternsat step 111. Additionally images can be converted from 2D to 3D, or from3D to 4D, if desired according to step 110. Persons of skill in the artwill appreciate that similar conversions can occur between 2D and 4D inthis process or any process illustrated in this invention.

As will be appreciated by those of skill in the art, the conversion stepis optional and the process can proceed directly from extracting imagedata from the ROI at step 107 to evaluating the data pattern at step111. Evaluating the data for patterns, includes, for example, performingthe measurements described in Table 1 or Table 2, above.

Additionally, the steps of locating the region of interest, obtainingimage data, and evaluating patterns can be performed once or a pluralityof times, respectively at any stage of the process. For example,following an evaluation of patterns at step 111, additional image datacan be obtained according to step 114, or another region of interest canbe located according to step 112. These steps can be repeated as oftenas desired, in any combination desirable to achieve the data analysisdesired.

An alternative process includes the step of enhancing image data priorto converting an image or image data to a 2D, 3D, or 4D pattern. Theprocess of enhancing image data, can be repeated if desired. In stillfurther embodiments, the step of enhancing image data may occur afterconverting an image or image data to a 2D, 3D, or 4D pattern. Again, theprocess of enhancing image data, can be repeated if desired.

Furthermore, in certain embodiments, after locating a part of the bodyfor study and imaging, the image is then converted to a 2D pattern, 3Dpattern or 4D pattern. The region of interest is optionally locatedwithin the image after optional conversion to a 2D, 3D and/or 4D imageand data is then extracted. Patterns are then evaluated in the extractedimage data.

Some or all the processes can be repeated one or more times as desired.For example, locating a part of the body for study, locating a region ofinterest, obtaining image data, and evaluating patterns, can be repeatedone or more times if desired, respectively. For example, following anevaluation of patterns, additional image data can be obtained, oranother region of interest can be located and/or another portion of thebody can be located for study. These steps can be repeated as often asdesired, in any combination desirable to achieve the data analysisdesired.

Image data may also be enhanced. The step of enhancing image data mayoccur prior to conversion, prior to locating a region of interest, priorto obtaining image data, or prior to evaluating patterns.

The method also comprises obtaining an image of a bone or a joint,optionally converting the image to a two-dimensional orthree-dimensional or four-dimensional pattern, and evaluating the amountor the degree of normal, diseased or abnormal tissue or the degree ofdegeneration in a region or a volume of interest using one or more ofthe parameters specified in Table 1 and/or Table 2. By performing thismethod at an initial time T₁, information can be derived that is usefulfor diagnosing one or more conditions or for staging, or determining,the severity of a condition. This information can also be useful fordetermining the prognosis of a patient, for example with osteoporosis orarthritis. By performing this method at an initial time T₁, and a latertime T₂, the change, for example in a region or volume of interest, canbe determined which then facilitates the evaluation of appropriate stepsto take for treatment. Moreover, if the subject is already receivingtherapy or if therapy is initiated after time T₁, it is possible tomonitor the efficacy of treatment. By performing the method atsubsequent times, T₂-T_(n). additional data can be acquired thatfacilitate predicting the progression of the disease as well as theefficacy of any interventional steps that have been taken. As will beappreciated by those of skill in the art, subsequent measurements can betaken at regular time intervals or irregular time intervals, orcombinations thereof. For example, it can be desirable to perform theanalysis at T₁ with an initial follow-up, T₂, measurement taken onemonth later. The pattern of one month follow-up measurements could beperformed for a year (12 one-month intervals) with subsequent follow-upsperformed at 6 month intervals and then 12 month intervals.Alternatively, as an example, three initial measurements could be at onemonth, followed by a single six month follow up which is then followedagain by one or more one month follow-ups prior to commencing 12 monthfollow ups. The combinations of regular and irregular intervals areendless, and are not discussed further to avoid obscuring the invention.

Moreover, one or more of the bone structure parameters listed in Tables1 and 2, and possibly one or more parameters, can be measured. Themeasurements can be analyzed separately or the data can be combined, forexample using statistical methods such as linear regression modeling orcorrelation. Actual and predicted measurements can be compared andcorrelated. See, also, Examples described later.

The method for predicting future fracture in a subject can be fullyautomated such that the measurements of one or more of the bonestructure parameters specified in Tables 1 and 2, and possibly one ormore other parameters, are done automatically without intervention. Aswill be appreciated by those of skill in the art, the fully automatedanalysis is, for example, possible with one or more of the stepsinvolved in predicting future fracture, including, sliding window ROIanalysis of such bone parameter(s) to generate bone parameter maps;watershed segmentation of parameter maps to identify possible or likelyfracture lines; local structure analysis (e.g., placement of ROI alongpredicted fracture line and analysis of traceular and cortical boneparameters); combining multiple bone parameters; and/or calculationssuch as multivariate regressions. This process may also include, forexample, seed growing, thresholding, atlas and model based segmentationmethods, live wire approaches, active and/or deformable contourapproaches, contour tracking, texture based segmentation methods, rigidand non-rigid surface or volume registration, for example based onmutual information or other similarity measures. One skilled in the artwill readily recognize other techniques and methods for fully automatedassessment of the parameters and measurements described herein.

Alternatively, the method of predicting future fractures in a subjectcan be semi-automated such that the measurements of one or more of theparameters, including at least one bone structure parameter, areperformed semi-automatically, i.e., with intervention. Thesemi-automatic assessment allows for human interaction and, for example,quality control, and utilizing the measurement of such parameter(s) todiagnose, stage, prognosticate or monitor a disease or to monitor atherapy. The semi-automated measurement is, for example, possible withimage processing techniques such as segmentation and registration. Thiscan include seed growing, thresholding, atlas and model basedsegmentation methods, live wire approaches, active and/or deformablecontour approaches, contour tracking, texture based segmentationmethods, rigid and non-rigid surface or volume registration, for examplebase on mutual information or other similarity measures. One skilled inthe art will readily recognize other techniques and methods forsemi-automated assessment of such parameters.

Following the step of deriving quantitative and/or qualitative imagedata, one or more candidate agents can be administered to the patient.The candidate agent can be any agent the effects of which are to bestudied. Agents can include any substance administered or ingested by asubject, for example, molecules, pharmaceuticals, biopharmaceuticals,agropharmaceuticals, or combinations thereof, including cocktails, thatare thought to affect the quantitative and/or qualitative parametersthat can be measured in a region of interest. These agents are notlimited to those intended to treat disease that affects themusculoskeletal system but this invention is intended to embrace any andall agents regardless of the intended treatment site. Thus, appropriateagents are any agents whereby an effect can be detected via imaging. Thesteps of locating a region of interest, obtaining image data, obtainingsuch quantitative and/or qualitative data from image data, andadministering a candidate agent, can be repeated one or more times asdesired, respectively. Image data may be enhanced as often as desired.

Furthermore, an image may be taken prior to administering the candidateagent. However, as will be appreciated by those of skill in the art, itis not always possible to have an image prior to administering thecandidate agent. In those situations, progress is determined over timeby evaluating the change in parameters from extracted image to extractedimage.

The derived quantitative and/or qualitative information can be comparedto an image taken at T1, or any other time, if such image is available.Again, the steps of deriving information and/or enhancing data can berepeated, as desired.

In addition, following the step of extracting image data from the ROI,the image can be transmitted. Transmission can be to another computer inthe network or via the World Wide Web to another network. Following thestep of transmitting the image, the image is converted to a pattern ofnormal and diseased tissue. Normal tissue includes the undamaged tissuelocated in the body part selected for study. Diseased tissue includesdamaged tissue located in the body part selected for study. Diseasedtissue can also include, or refer to, a lack of normal tissue in thebody part selected for study. For example, damaged or missing bone wouldbe considered diseased tissue. Once the image is converted, it may beanalyzed.

The step of transmitting the image is optional. As will be appreciatedby those of skill in the art, the image can also be analyzed prior toconverting the image to a pattern of normal and diseased.

As previously described, some or all the processes can be repeated oneor more times as desired. For example, locating a region of interest,obtaining image data, enhancing image data, transmitting an image,converting the image to a pattern of normal and diseased, analyzing theconverted image, can be repeated one or more times if desired,respectively.

Two or more devices may be connected. Either the first or second devicecan develop a degeneration pattern from an image of a region ofinterest. Similarly, either device can house a database for generatingadditional patterns or measurements. The first and second devices cancommunicate with each other in the process of analyzing an image,developing a degeneration pattern from a region of interest in theimage, creating a dataset of patterns or measurements or comparing thedegeneration pattern to a database of patterns or measurements. However,all processes can be performed on one or more devices, as desired ornecessary.

In this method the electronically generated, or digitized image orportions of the image can be electronically transferred from atransferring device to a receiving device located distant from thetransferring device; receiving the transferred image at the distantlocation; converting the transferred image to a pattern of normal ordiseased or abnormal tissue using one or more of the parametersspecified in Table 1 or Table 2; and optionally transmitting the patternto a site for analysis. As will be appreciated by those of skill in theart, the transferring device and receiving device can be located withinthe same room or the same building. The devices can be on a peer-to-peernetwork, or an intranet. Alternatively, the devices can be separated bylarge distances and the information can be transferred by any suitablemeans of data transfer, including http and ftp protocols.

Alternatively, the method can comprise electronically transferring anelectronically-generated image or portions of an image of a bone or ajoint from a transferring device to a receiving device located distantfrom the transferring device; receiving the transferred image at thedistant location; converting the transferred image to a degenerationpattern or a pattern of normal or diseased or abnormal tissue using oneor more of the parameters specified in Table 1 or Table 2; andoptionally transmitting the degeneration pattern or the pattern ofnormal or diseased or abnormal, tissue to a site for analysis.

Thus, the invention described herein includes methods and systems forprognosis of fracture risk. (See, also, Examples).

In order to make more accurate prognoses, it may be desirable in certaininstances to compare data obtained from a subject to a referencedatabase. For example, when predicting fracture risk, it may be usefulto compile data of actual (known) fracture load in a variety of samplesand store the results based on clinical risk factors such as age, sexand weight (or other characteristics) of the subject from which thesample is obtained. The images of these samples are analyzed to obtainparameters shown in Tables 1 and 2, and possibly one or more otherparameters. A fracture risk model correlated with fracture load may bedeveloped using univariate, bivariate and/or multivariate statisticalanalysis of these parameters and is stored in this database. A fracturerisk model may include information that is used to estimate fracturerisk from parameters shown in Tables 1 and 2, and possibly one or moreother parameters. An example of a fracture risk model is thecoefficients of a multivariate linear model derived from multivariatelinear regression of these parameters (Tables 1, 2, age, sex, weight,etc.) with fracture load. A person skilled in the art will appreciatethat fracture risk models can be derived using other methods such asartificial neural networks and be represented by other forms such as thecoefficients of artificial neural networks. Patient fracture risk canthen be determined from measurements obtain from bone images byreferencing to this database.

In conventional methods of determining actual fracture load,cross-sectional images may be taken throughout testing to determine atwhat load force a fracture might occur.

The analysis techniques described herein can then be applied to asubject and the risk of fracture (or other disease) could be predictedusing one or more of the parameters described herein. Theprognostication methods described herein are more accurate thanconventional methods for predicting fracture risk. FIG. 5A is a graphdepicting conventional linear, regression analysis of DXA bone mineraldensity correlated to fracture load. Correlations of individualparameters to fracture load are comparable to DXA. However, whenmultiple structural parameters are combined, the prediction of load atwhich fracture will occur is more accurate. Thus, the analyses of imagesas described herein can be used to accurately predict musculoskeletaldisease such as fracture risk.

Another aspect of the present invention is a kit for aiding inpredicting fracture risk in a subject, which kit comprises a softwareprogram, which when installed and executed on a computer creates a boneparameter map (e.g., using one or more of the parameters specified inTables 1 and 2, and possibly one or more other parameters) presented ina standard graphics format and produces a computer readout. The kit canfurther include software for (1) identifying likely fracture lines(e.g., by watershed segmentation); (2) placing one or more ROI alongpredicted fracture line(s); (3) analyzing one or more bone parametersalong predicted fracture lines; and/or (4) combining multiple boneparameters and calculating fracture load.

The kit can further include one or more databases of measurements foruse in calibrating or diagnosing the subject. One or more databases canbe provided to enable the user to compare the results achieved for aspecific subject against, for example, a wide variety of subjects, or asmall subset of subjects having characteristics similar to the subjectbeing studied.

A system is provided that includes (a) a device for electronicallytransferring an image, a parameter map, an analyzed parameter map, etc.,to a receiving device located distant from the transferring device; (b)a device for receiving the image or map at the remote location; (c) adatabase accessible at the remote location for generating additionalpatterns or measurements for the bone or the joint of a subject whereinthe database includes a collection of subject patterns or data, forexample of human bones or joints, which patterns or data are organizedand can be accessed by reference to characteristics such as type ofjoint, gender, age, height, weight, bone size, type of movement, anddistance of movement; and (d) optionally a device for transmitting thecorrelated pattern back to the source of the degeneration pattern orpattern of normal, diseased or abnormal tissue.

Thus, the methods and systems described herein may make use ofcollections of data sets of measurement values, for example measurementsof bone structure, probably with other measurements from images (e.g.,x-ray images). Records can be formulated in spreadsheet-like format, forexample including data attributes such as date of image (x-ray), patientage, sex, weight, current medications, geographic location, etc. Thedatabase formulations can further comprise the calculation of derived orcalculated data points from one or more acquired data points, typicallyusing the parameters listed in Tables 1 and 2 or combinations thereof. Avariety of derived data points can be useful in providing informationabout individuals or groups during subsequent database manipulation, andare therefore typically included during database formulation. Deriveddata points include, but are not limited to the following: (1) maximumvalue of a selected bone structure parameter, determined for a selectedregion of bone or joint or in multiple samples from the same ordifferent subjects; (2) minimum value of a selected bone structureparameter, determined for a selected region of bone or joint or inmultiple samples from the same or different subjects; (3) mean value ofa selected bone structure parameter, determined for a selected region ofbone or joint or in multiple samples from the same or differentsubjects; (4) the number of measurements that are abnormally high orlow, determined by comparing a given measurement data point with aselected value; and the like. Other derived data points include, but arenot limited to the following: (1) maximum value of bone mineral density,determined for a selected region of bone or in multiple samples from thesame or different subjects; (2) minimum value of bone mineral density,determined for a selected region of bone or in multiple samples from thesame or different subjects; (3) mean value of bone mineral density,determined for a selected region of bone or in multiple samples from thesame or different subjects; (4) the number of bone mineral densitymeasurements that are abnormally high or low, determined by comparing agiven measurement data point with a selected value; and the like. Otherderived data points will be apparent to persons of ordinary skill in theart in light of the teachings of the present specification. The amountof available data and data derived from (or arrived at through analysisof) the original data provides an unprecedented amount of informationthat is very relevant to management of musculoskeletal-related diseasessuch as osteoporosis or arthritis. For example, by examining subjectsover time, the efficacy of medications can be assessed.

Measurements and derived data points are collected and calculated,respectively, and can be associated with one or more data attributes toform a database.

Data attributes can be automatically input with the electronic image andcan include, for example, chronological information (e.g., DATE andTIME). Other such attributes can include, but are not limited to, thetype of imager used, scanning information, digitizing information andthe like. Alternatively, data attributes can be input by the subjectand/or operator, for example subject identifiers, i.e., characteristicsassociated with a particular subject. These identifiers include but arenot limited to the following: (1) a subject code (e.g., a numeric oralpha-numeric sequence); (2) demographic information such as race,gender and age; (3) physical characteristics such as weight, height andbody mass index (BMI); (4) selected aspects of the subject's medicalhistory (e.g., disease states or conditions, etc.); and (5)disease-associated characteristics such as the type of bone disorder, ifany; the type of medication used by the subject. In the practice of thepresent invention, each data point would typically be identified withthe particular subject, as well as the demographic, etc. characteristicof that subject.

Other data attributes will be apparent to persons of ordinary skill inthe art in light of the teachings of the present specification. (See,also, WO 02/30283).

Thus, data about bond structure information, possibly with bone mineraldensity information and/or articular information, is obtained fromnormal control subjects using the methods described herein. Thesedatabases are typically referred to as “reference databases” and can beused to aid analysis of any given subject's image, for example, bycomparing the information obtained from the subject to the referencedatabase. Generally, the information obtained from the normal controlsubjects will be averaged or otherwise statistically manipulated toprovide a range of “normal” measurements. Suitable statisticalmanipulations and/or evaluations will be apparent to those of skill inthe art in view of the teachings herein. The comparison of the subject'sinformation to the reference database can be used to determine if thesubject's bone information falls outside the normal range found in thereference database or is statistically significantly different from anormal control.

Data obtained from images, as described above, can be manipulated, forexample, using a variety of statistical analyses to produce usefulinformation. Databases can be created or generated from the datacollected for an individual, or for a group of individuals, over adefined period of time (e.g., days, months or years), from derived data,and from data attributes.

For example, data can be aggregated, sorted, selected, sifted, clusteredand segregated by means of the attributes associated with the datapoints. A number of data mining software exist which can be used toperform the desired manipulations.

Relationships in various data can be directly queried and/or the dataanalyzed by statistical methods to evaluate the information obtainedfrom manipulating the database.

For example, a distribution curve can be established for a selected dataset, and the mean, median and mode calculated therefor. Further, dataspread characteristics, e.g., variability, quartiles, and standarddeviations can be calculated.

The nature of the relationship between any variables of interest can beexamined by calculating correlation coefficients. Useful methods fordoing so include, but are not limited to: Pearson Product MomentCorrelation and Spearman Rank Correlation. Analysis of variance permitstesting of differences among sample groups to determine whether aselected variable has a discernible effect on the parameter beingmeasured.

Non-parametric tests can be used as a means of testing whethervariations between empirical data and experimental expectancies areattributable to chance or to the variable or variables being examined.These include the Chi Square test, the Chi Square Goodness of Fit, the2×2 Contingency Table, the Sign Test and the Phi CorrelationCoefficient. Other tests include z-scores, T-scores or lifetime risk forarthritis, cartilage loss or osteoporotic fracture.

There are numerous tools and analyses available in standard data miningsoftware that can be applied to the analyses of the databases that canbe created according to this invention. Such tools and analysis include,but are not limited to, cluster analysis, factor analysis, decisiontrees, neural networks, rule induction, data driven modeling, and datavisualization. Some of the more complex methods of data miningtechniques are used to discover relationships that are more empiricaland data-driven, as opposed to theory driven, relationships.

Statistical significance can be readily determined by those of skill inthe art. The use of reference databases in the analysis of imagesfacilitates that diagnosis, treatment and monitoring of bone conditionssuch as osteoporosis.

For a general discussion of statistical methods applied to dataanalysis, see Applied Statistics for Science and Industry, by A. Romano,1977, Allyn and Bacon, publisher.

The data is preferably stored and manipulated using one or more computerprograms or computer systems. These systems will typically have datastorage capability (e.g., disk drives, tape storage, optical disks,etc.). Further, the computer systems can be networked or can bestand-alone systems. If networked, the computer system would be able totransfer data to any device connected to the networked computer systemfor example a medical doctor or medical care facility using standarde-mail software, a central database using database query and updatesoftware (e.g., a data warehouse of data points, derived data, and dataattributes obtained from a large number of subjects). Alternatively, auser could access from a doctor's office or medical facility, using anycomputer system with Internet access, to review historical data that canbe useful for determining treatment.

If the networked computer system includes a World Wide Web application,the application includes the executable code required to generatedatabase language statements, for example, SQL statements. Suchexecutables typically include embedded SQL statements. The applicationfurther includes a configuration file that contains pointers andaddresses to the various software entities that are located on thedatabase server in addition to the different external and internaldatabases that are accessed in response to a user request. Theconfiguration file also directs requests for database server resourcesto the appropriate hardware, as can be necessary if the database serveris distributed over two or more different computers.

As a person of skill in the art will appreciate, one or more of theparameters specified in Table 1 and Table 2 can be used at an initialtime point T₁ to assess the severity of a bone disease such asosteoporosis. The patient can then serve as their own control at a latertime point T₂, when a subsequent measurement using one or more of thesame parameters used at T₁ is repeated.

A variety of data comparisons can be made that will facilitate drugdiscovery, efficacy, dosing, and comparisons. For example, one or moreof the parameters specified in Table 1 and Table 2 may be used toidentify lead compounds during drug discovery. For example, differentcompounds can be tested in animal studies and the lead compounds withregard to the highest therapeutic efficacy and lowest toxicity, e.g. tothe bone or the cartilage, can be identified. Similar studies can beperformed in human subjects, e.g., FDA phase I, II or III trials.Alternatively, or in addition, one or more of the parameters specifiedin Table 1 and Table 2 can be used to establish optimal dosing of a newcompound. It will be appreciated also that one or more of the parametersspecified in Table 1 and Table 2 can be used to compare a new drugagainst one or more established drugs or a placebo. The patient can thenserve as their own control at a later time point T₂.

EXAMPLES Example 1 Correlation of Micro-Structural and Macro-StructuralParameters to Fracture Load

Using 15 fresh cadaveric femurs, the following analyses were performedto determine the correlation of various micro-structural andmacro-structural parameters to fracture load, as determined bybiomechanical testing. Parameters measured included one or more of thefollowing

Parameter Name Description Measurements on gray values of extractedstructures Std. dev. of Normalized ROI is subtracted from the backgroundusing a difference of normalized ROI gaussian filter. The standarddeviation reflects the “roughness” of the trabecular structures.Measurements on binarization of extracted structures Trab. PerimeterTotal length of outline (perimeter) of extracted trabecular structuresin a ROI. Trab. Trabecular perimeter normalized by area of extractedtrabecular structures. Perimeter/Trab. Area Trab. Trabecular perimeternormalized by ROI area. Perimeter/Total Area Trabecular Bone Change ofperimeter per change of area. Measures concavity and convexity PatternFactor of structures. Trabecular Star Estimated volume of trabecularstructures by measuring distance of random Volume points to boundariesof extracted structures. Marrow Space Mean length of skeletonized marrowspace (background) region. Length Mode Trab. The mode of distancetransform values of the marrow space (background) Separation region.Std. Dev of The standard deviation of distance transform values of themarrow space Trabecular (background) region. Separation Trabecular Themean of distance transform values of the marrow space region. SeparationTrabecular The mean of distance transform values along the skeleton(centerline) of Thickness extracted structures. Max. Trab. The maximumdistance transform value of extracted structures in an ROI. ThicknessMeasurements on skeleton of extracted structures Trabecular The mean ofdistance transform values along the segmented (by nodes) Segmentskeleton of extracted structures. Thickness Free-end Segment The mean ofdistance transform values along the free-end segments of the Thicknessskeletonized structures. Node-Node The mean of distance transform valuesalong the node-node (inner) Segment segments of the skeletonizedstructures. Thickness Number of Nodes Number of nodes (branching points)of skeletonized structures normalized by ROI area. Segment Number Numberof skeleton segments normalized by ROI area. Free-end Segment Number offree-end skeleton segments normalized by ROI area. Number Segment Ratioof length of segments to distance between segment ends. TortuositySegment Solidity Ratio of length of segment to area of convex hull ofthe segment. Watershed segmentation is applied to normalized gray levelimages. Statistics of watershed segments are: Watershed Average area ofwatershed segments. Measures the trabecular separation by Segment Areaarea between structures. Watershed Number of watershed segmentsnormalized by ROI area. Segment Number Std. dev. of Standard deviationof areas of watershed segments. Measures the Watershed Area homogeneityof trabecular separation Macro-anatomical and geometric parametersMedian Cortical Median of distance transform values measured along thecenterline of Thickness extracted cortical bone structure. MaximumMaximum of distance transform values measured along the centerline ofCortical extracted cortical bone structure. Thickness Hip Axis LengthLength of the femoral neck axis, extending from the bone edge at thebase of trochanter to the bone edge at the inner pelvic brim (femoralhead for cadaveric femur). Neck-shaft angle Angle between femoral neckaxis and shaft axis. Head diameter Largest cross section of femoralhead. Mean Neck Width Mean of distance transform on femoral neck axisbetween center of femoral head to intertrochanteric line. Minimum NeckMinimum distance transform value on femoral neck axis. Width

Standardization of Hip Radiographs:

Density and magnification calibration on the x-ray radiographs wasachieved using a calibration phantom. The reference orientation of thehip x-rays was the average orientation of the femoral shaft.

Automatic Placement of Regions of Interest.

Seven regions of interest were consistently and accurately placed basedon the geometry and position of the proximal femur (FIG. 2). This wasachieved by detecting femoral boundaries, estimating shaft and neckaxes, and constructing the ROIs based on axes and boundary interceptpoints. This approach ensured that the size and shape of ROIs placedconformed to the scale and shape of the femur, and thus were consistentrelative to anatomic features on the femur.

Automatic Segmentation of the Proximal Femur:

A global gray level thresholding using bi-modal histogram segmentationalgorithm(s) was performed on the hip images and a binary image of theproximal femur was generated. Edge-detection analysis was also performedon the hip x-rays, including edge detection of the outline of theproximal femur that involved breaking edges detected into segments andcharacterizing the orientation of each segment. Each edge segment wasthen referenced to a map of expected proximal femur edge orientation andto a map of the probability of edge location. Edge segments that did notconform to the expected orientation or which were in low probabilityregions were removed. Morphology operations were applied to the edgeimage(s) to connect any discontinuities. The edge image formed anenclosed boundary of the proximal femur. The region within the boundarywas then combined with the binary image from global thresholding to formthe final mask of the proximal femur.

Automatic Segmentation and Measurement of the Femoral Cortex:

Within a region of interest (ROI), edge detection was applied.Morphology operations were applied to connect edge discontinuities.Segments were formed within enclosed edges. The area and the major axislength of each segment were then measured. The regions were alsosuperimposed on the original gray level, image and average gray levelwithin each region was measured. The cortex was identified as thosesegments connected to the boundary of the proximal femur mask with thegreatest area, longest major axis length and a mean gray level about theaverage gray level of all enclosed segments within the proximal femurmask.

The segment identified as cortex was then skeletonized. The orientationof the cortex skeleton was verified to conform to the orientation map ofthe proximal femur edge. Euclidean distance transform was applied to thebinary image of the segment. The values of distance transform valuealong the skeleton were sampled and their average, standard deviation,minimum, maximum and mod determined.

Watershed Segmentation for Characterizing Trabecular Structure:

Marrow spacing was characterized by determining watershed segmentationof gray level trabecular structures on the hip images, essentially asdescribed in Russ “The Image Processing Handbook,” 3^(rd). ed. pp.494-501. This analysis takes the gray level contrast between the marrowspacing and adjacent trabecular structures into account. The segments ofmarrow spacing generated using watershed segmentation were measured forthe area, eccentricity, orientation, and the average gray level on thex-ray image within the segment. Mean, standard deviation, minimum,maximum and mode were determined for each segment. In addition, variousmicro-structural and/or macro-anatomical parameters were assessed forseveral ROIs to predict the fracture path, as shown in FIG. 11.

Measurement of Femoral Neck BMD:

MA analysis of bone mineral density was performed in the femoral neckregion of the femurs.

Biomechanical Testing of Intact Femur:

Each cadaveric femur sample (n=15) was tested for fracture load asfollows. First, the femur was placed at a 15° angle of tilt and an 8°external rotation in an Instron 1331 Instrument (Instron, Inc.) and aload vector at the femoral head simulating single-leg stance wasgenerated, essentially as described in Cheal et al. (1992) J. Orthop.Res. 10(3):405-422. Second, varus/valgus and torsional resistivemovements simulating passive knee ligament restraints were applied.Next, forces and movement at failure were measured using a six-degree offreedom load cell. Subsequently, a single ramp, axial compressive loadwas applied to the femoral head of each sample at 100 mm/s untilfracture. Fracture load and resultant equilibrium forces and moments atthe distal end of the femur were measured continuously.

There was a weak positive correlation of femoral neck BMD (r=0.34,p=0.10) (FIG. 4A) and total BMD with failure load (r=0.28, p=0.15).Radiographs were analyzed in several regions of interest (ROI) at thefemoral head, neck and proximal shaft to yield indices of trabecularmicro-structure and macro-anatomic indices such as cortical thickness.The micro-structural parameter of Trabecular Segment Thickness from ROI4 had the strongest failure load correlation coefficient, with r=−0.75.Macro-anatomic indices such as Maximum Cortical Thickness of ROI 6 andMedian Cortical Thickness of ROI 5 correlate with failure load withr=0.65 (p=0.005) and r=0.53 (p=0.02), respectively.

Based on these results, Trabecular Segment Thickness and TrabecularSeparation from ROI 4 were combined to predict a failure load. Based onresults from these 15 femora, correlation between predicted and actualfailure loads was r=0.8 (p<0.001) (FIG. 5B). The mean fracture load was5.4 kiloNewton with a standard deviation of 2.3 kiloNewton. Thesestatistics and the coefficients of multivariate linear regression werestored as data of the fracture load reference database.

Influence of Positioning:

The effects of the femur position were also examined in order todetermine a set of measurements that are the most robust against thepositioning variations that can occur during imaging.

Radiographs were taken at −15° (external rotation), and at every 5°increment up to +20° of internal rotation (70 kVp, photo-timer, centeredon the femoral neck). Variability of a parameter was expressed as thecoefficient of variation (COY) of the measurements at each angle. Of allthe regions, ROI 4 showed the lowest average (root mean square) COV ofthe parameters.

As shown in Table 3 below, variability was generally lower for the 5° to15° range. This was also observed for the other regions of interest.Thus, internal rotation of 5° to 15° provides an acceptable margin ofvariation for a number of parameters. The regular AP hip x-ray imagingprotocol used by technicians is therefore sufficient to controlpositioning variability.

TABLE 3 Range of Rotation angles (degrees) Parameter 0 . . . 10 5 . . .15 5 . . . 20 10 . . . 20 −15 . . . 20 Trab. 1.6 0.5 1.7 1.9 2.7Perimeter/ Total Area Free-end 2.6 0.5 2.7 3.3 3.7 Thickness Segment 0.81.0 0.9 0.7 0.8 Tortuosity Trabecular 2.5 1.0 3.6 4.1 3.0 SegmentThickness Trabecular 2.5 1.2 6.9 7.7 5.3 Area Ratio Trabecular 1.3 1.34.1 4.4 3.8 Bone Pattern Factor Trabecular 1.9 2.0 1.6 0.6 1.8Separation Segment 4.1 2.6 4.7 5.4 3.9 Solidity

Influence of Radiographic Exposure Settings:

The influence of hip x-ray exposure variations on image quality and thesubsequent analysis of structural parameters were also tested.

The right hip of a frozen cadaver pelvis was imaged with 60 kVp, 70 kVp,and 80 kVp at 150 mA with automatic exposure using the photo-timer,followed by an exposure one step below and another at one step above theauto exposure, in terms of mAs. An additional image was taken at 75 kVpwith the photo timer.

Most parameters exhibited the least variation (across mAs) at 60 kVp,with variability growing with increased kVp. Trabecular separationmeasurements in ROI 7 had COV's of 2.1%, 4.2% and 5.1% at 60 kVp, 70 kVpand 80 kVp, respectively. These may represent the variability to beexpected when using manual time settings in the absence of the automaticphototimer function.

When measurements from only the images captured using the automaticphototimer were considered across kVp (60, 70, 75, 80), the mostreproducible measurement was trabecular perimeter, with an average COVof 1.9%.

Our results indicate that the use of a phototimer can markedly reducethe variability of exposures due to subjective kVp setting choices.Radiographs produced with proper and consistent use of the phototimerhad acceptable variations of micro-structural and macro-anatomicalmeasurements.

Example 2 Correlation of 2D and 3D Measurements

To demonstrate that these methods that use 2D x-ray technology toquantitatively assess trabecular architecture are as effective as 3DμCT, which serves as a gold standard for such measurements, thefollowing experiments were performed. Bone cores (n=48) were harvestedfrom cadaveric proximal femora. Specimen radiographs were obtained and2D structural parameters were measured on the radiographs. Cores werethen subjected to 3D μCT and biomechanical testing. The μCT images wereanalyzed to obtained 3D micro-structural measurements. Digitized 2Dx-ray images of these cores were also analyzed as described herein toobtain comparative micro-structural measurements.

Results showed very good correlation among the numerous 2D parametersand 3D μCT measurements, including for example correlation between 2DTrabecular Perimeter/Trabecular Area (Tb.P/Tb.A) with 3D BoneSurface/Bone Volume (r 0.92, p<0.001), and 2D Trabecular Separation(Tb.Sp) with 3D Trabecular Separation (r=0.88, p<0.001), as shown inFIG. 3. The 2D Tb.P/Tb.A and 2D Tb.Sp also function correlate very wellas predictive parameters for the mechanical loads required to fracturethe cores, with r=−0.84 (p<0.001) and r=−0.83 (p<0.001), respectively,when logarithmic and exponential transformations were used in theregression, as shown in FIG. 4.

These results demonstrate that 2D micro-structural measurements oftrabecular bone from digitized radiographs are highly correlated with 3Dmeasurements obtained from μCT images. Therefore, the mechanicalcharacteristics of trabecular bone micro-structure from digitizedradiographic images can be accurately determined from 2D images.

Example 3 Sliding Window Analysis and Watershed

Segmentation

To show feasibility of the approach to better predict an individual'sfailure load and fracture risk, a sliding window analysis of theproximal femur in 3 cadaveric samples was also performed. Instead ofusing fixed ROI's as described in FIG. 2, a regular grid was laid overthe proximal femur in the x-ray taken before the mechanical failuretests. A rectangular region of interest of a fixed size was placed ateach grid node, and bone structure parameters were evaluated within theboundaries of the ROI at each position. This resulted in a value foreach bone parameter at each grid node, which was displayed in acolor-coded map of the proximal femur for each parameter.

The pre-fracture x-rays were then aligned with the post-fracture images,so that the fracture lines can be shown in the color maps, as shown inFIG. 6. It can be seen in the samples presented in FIG. 6 that certainparameters (e.g., for Area Ratio and Trabecular Perimeter) have a verygood agreement between low values (“valleys”) in the color maps and thefracture lines, suggesting that a sliding window bone structuralanalysis can be used to generate a prediction of the exact locationwhere the bone will fracture. The valleys can be found by applying awatershed transformation to the negative values of the parameter maps.For other bone parameters that indicate bone weakness with high values,the watershed transformation can be applied directly to the map.

Example 4 Prediction of Fracture Risk Using Fracture Load ReferenceDatabase

A hip x-ray of a cadaver pelvis was exposed using standard clinicalprocedure and equipment. The radiograph film was developed anddigitized. The image was then analyzed to obtain micro-structure, andmacro-anatomical parameters. The local maximum spacing, standarddeviation of cortical thickness of ROI 3, maximum cortical thickness ofROI 5, and mean node-free end length for ROI 3 were used to predict loadrequired to fracture the cadaver hip using the coefficients ofmultivariate linear regression stored in the fracture load referencedatabase. The predicted fracture load was 7.5 kiloNewton. This fractureload is 0.98 standard deviation above the average of the fracture loadreference database (or z-score=0.98). This result may suggest that thesubject had a relatively low risk of sustaining a hip fracture ascompared to the population of the reference database.

Example 5 Prediction of Hip Fracture Risk from Radiographic

Images

Individualized hip fracture risk is determined as shown in FIG. 7A.Briefly, an x-ray of the hip is taken at step 701.

At step 702, a 2D fracture line is predicted. The micro- andmacro-architecture of the proximal femur in the image is determined byperforming automated analyses, as described in Examples 1 and 4.Algorithms for analysis of density, length, thickness, and orientationof trabeculae as well as cortical bone thickness in an ROI in theradiograph are developed using Matlab (The MathWorks, Inc., Natick,Mass.). Similarly, software is developed for automated sliding windowanalysis (Example 3) of parameters, including at least one bonestructure parameter, to produce a distribution map of the proximal femurfor each parameter.

In addition, local abnormalities of bone structure are determined fromthe parameters maps generated as described in Example 3. Regions of highor low values will be evaluated to determine bone strength patterns andused to predict a location of hip fracture.

The parameter maps generated as described in Example 3 are used toidentify regions on the bone that have abnormal local structuralproperties. These regions of high or low values can indicate patternsfor stronger or weaker characteristics of bone. The parameter mapsgenerated from the hip x-ray provide a spectrum of trabecularcharacteristics and can be interpreted as spatial distributions of bonestrength. They will be used to predict the location of a hip fracture.

In a first step, depending on the kind of parameter, the low values(“valleys”) or high values (“ridges”) on the parameter maps are tracedusing a watershed segmentation operation (see FIG. 8). The resultingboundaries between the regions are regarded as potential fracture lines.

In a second step, the path along the potential fracture lines that ismost likely to coincide with the actual fracture location is determined.A two-dimensional curved beam model as described by Mourtada et al.(1996) J Orthop Res. 14(3):483-492 is applied on the thresholded x-rayimage of the proximal femur. First, the femoral shaft and neck areapproximated by linear axes. The axis in the intertrochanteric region isapproximated by a hyperbolic curve that is asymptotic to the linear axesof the femoral neck and shaft, with the focus point at the neck-shaftangle bisector (FIG. 9). Given a loading condition, the internal bendingmoment, M, is calculated along the neutral axis at 1 mm intervals. Forregions where curvature is negligible, the normal stresses along theboundary of the femur are calculated by Equation (1):

$\begin{matrix}{\sigma_{n} = \frac{M \cdot x}{CSMI}} & (1)\end{matrix}$where CSMI is cross-sectional moment of inertia, and x is the distancefrom the neutral axis to the point where stress is calculated. Sincepeak stresses occur on the surfaces, x will be the perpendiculardistance of the surface boundary to the neutral axis. Along thecurvature, normal stresses can be calculated using Equation (2),

$\begin{matrix}{\sigma_{n} = \frac{M \cdot x}{{CSA} \cdot e \cdot \left( {R_{na} - x} \right)}} & (2)\end{matrix}$where CSA is the cross-sectional area, e is the distance between thecentroid axis and neutral axis, and R_(na) is the radius of curvature ofthe neutral axis. The loading condition applied to the curved beam modelwill simulate a fall on the side from standing height with the estimatedforces obtained using the methods below. Both CSMI and CSA can beestimated for each cross section by integrating the optical density overthe section profile. The relative density and stress values aresufficient for the purpose of locating fracture location. The softtissue variation was assumed to be insignificant over the proximalfemur.

Two common types of fracture, intertrochanteric and femoral neck, willbe considered. Two tensile peaks are known to exist on the medialsurface for the fall loading condition (see, e.g., Mourtada et al.,supra). The peak closer to the bisector of the neck-shaft angle isidentified as the starting point of the intertrochanteric fracture, andthe other one that is known to be on the posterior surface of the neck,as the starting point for the femoral neck fracture. The cross-sectionallines corresponding to the position of tensile peaks will be considered.The predicted fracture paths will be traced by selecting the watershedboundary segments that are closest to the selected cross-sectional lines(FIG. 10).

To predict the likelihood of intertrochanteric or femoral neck fracture,the values of the parameter map underlying the selected fracture pathswill be evaluated and compared. The more likely fracture path will havea lower mean value of a parameter that represents bone strength asoptimized by cadaver mechanical loading tests.

At step 703, a local micro- and macro-structural analysis along thepredicted fracture line is performed to estimate the load at which thebone will fracture in a particular falling scenario. The case-specificROI is placed around the predicted fracture line in the trabecular boneusing a region growing technique with value constraints. Cortical boneparameters are evaluated in the areas adjacent to the trabecular boneROI with boundaries determined by perpendicular projection of the outercontact points between trabecular ROI and cortical bone onto the outercontour of the cortical bone (FIG. 11). Multivariate regression willthen be used to calculate a failure load F_(failure) from the results ofthe different bone parameter analyses.

The risk of sustaining an osteoporotic hip fracture does not only dependon the femoral failure load, but also on the impact on the femur in afall. Factors that influence the severity of the impact are, amongothers, soft tissue thickness, standing height, and body mass. Impactdecreases with increasing soft tissue covering the hip, while it isincreased with greater standing height or body mass. The body habitus iscalculated at step 704.

Estimation of femoral impact is performed essentially as described inKroonenberg et al. (1995) J. Biomech. Eng. 117(3):309-318. Calculationsshown in Equations. (3)-(8) below are based on studies of women.Equations for men can be derived accordingly.

The hip impact velocity is given by Equation (3),V=2.72√{square root over (h)}  (3),where h is the full body height. The effective mass, i.e., the mass ofthe part of the body that contributed to the impact force on the hip iscalculated as shown in Equation (4):

$\begin{matrix}{M = {\frac{7}{20}m}} & (4)\end{matrix}$where m is the total body mass.

The peak force on the greater trochanter can then be approximated byEquation (5), at step 705:F _(peak) =V√{square root over (kM)}=1.6√{square root over (hmk)}  (5)

The soft tissue stiffness k correlates negatively with soft tissuethickness (see, Robinovitch et al. (1991)J. Biomech. Eng 113:366-374).Fitting the data obtained by Robinovitch et al. for loads with 100 Nwith a power curve, Equation (6) is obtained:k=486x ^(−0.83)  (6)with x being the soft tissue thickness. Soft tissue stiffness dependencyon loading force can be approximated by Equation (7):

$\begin{matrix}{k = {71060 \cdot \left( {1 - e^{\frac{- F}{151}}} \right)}} & (7)\end{matrix}$

Using Equation. (7) for 100 N, k=34415 N/m for women. Since withEquation (7) soft tissue stiffness plateaus at 71 kN/m for loadsexpected in a fall, Equation (6) by 71000/34415=2.1 to obtain therelationship between soft tissue thickness and soft tissue stiffness forthese higher loads as given by Equation (8):k=1021x ^(−0.83)  (8)

The soft tissue thickness x will be measured in the hip x-ray laterallybetween the greater trochanter and the skin.

As discussed above with Example 4, a fracture load reference databasecan be used for more accurate determination of the fracture load.

At step 710, a measure of fracture risk can then be calculated as theratio of the peak impact force obtained at step 705 via Equation (5) andthe predicted failure load obtained at step 703:IFRI (Individualized Fracture Risk Index)=F _(peak) /F _(failure)  (9)

Thus, when the Individualized Fracture Risk Index is low (IFRI<<1), theforces applied to the femur are much lower than required to fracture it,and the bone is at low risk of failure. However, when the IFRI is high(IFRI>>1), failure of the bone is predicted to occur.

FIG. 7B summarizes the procedure for predicting hip failure load from anx-ray of the proximal femur. As shown, step 702 for predicting fracturelines includes two sub-steps 7021 and 7022. Sub-step 7021 uses slidingROI analysis to analyze bone parameters, including at least one bonestructure parameter, for each sampling point and generate bone parametermaps. Sub-step 7022 uses watershed segmentation of parameter maps toidentify most likely fracture lines. Step 703 for predicting the failureload includes sub-steps 7031 and 7032. Sub-step 7031 places ROI alongpredicted fracture line and analyze trabecular and cortical boneparameters; and sub-step 7032 combines multiple bone parameters andcalculates fracture load using multivariate regression.

FIG. 12 is a block diagram of a computer program used to predictfracture risk according to one embodiment of the present invention. Asshown, a module 1201 receives images of skeleton parts of patientsand/or references. A module 1202 receives the images from the module1201 and derives bone structure parameters therefrom. A module 1203measures a fracture load of a skeleton part of a reference. A module1204 correlates the reference's fracture load to the reference's bonestructure parameter, taking clinical risk factors of the reference intoconsideration. A module 1205 controls storage of the correlation. Amodule 1206 generates a parameter map from the derived bone structureparameter of a patient to predict a fracture line. A module 1207receives the correlation from the module 1204 and the fracture line fromthe module 1206, and calculates the fracture load of the patient. Amodule 1208 estimates a body habitus of the patient. A module 1209receives the body habitus estimation from the module 1208 and thefracture load of the patient from module 1207, and calculates a peakinput force. The risk of fracture is predicted at a module 1210 bycalculating the ratio between the fracture load of the patient from themodule 1207 and the peak impact force from the module 1209.

Although FIG. 12 illustrates modules of a computer program, a skilledartisan would appreciate that hardware and firmware could be used torealize the functions of the modules, and the modules could bedistributed at different locations. A suitably programmed computer canconstitute hardware counterparts of each of the modules in FIG. 12.

The foregoing description of embodiments of the present invention hasbeen provided for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Many modifications and variations will be apparent tothe practitioner skilled in the art. The embodiments were chosen anddescribed in order to best explain the principles of the invention andits practical application, thereby enabling others skilled in the art tounderstand the invention and the various embodiments and with variousmodifications that are suited to the particular use contemplated. It isintended that the scope of the invention be defined by the followingclaims and their equivalents.

The invention claimed is:
 1. A method for predicting fracture risk usingelectronic image data of a part of a skeleton of a target in a computersystem, the method comprising: analyzing at least one bone structureparameter corresponding to at least a portion of the target in one ormore regions of interest (ROIs); generating a parameter map from ameasurement for the at least one bone structure parameter to predict afracture line; analyzing the at least one bone structure parameter alongthe predicted fracture line to predict a fracture load at which afracture will occur; and predicting a fracture risk by calculating aratio between the fracture load and a peak impact force on the part ofthe skeleton of the target using a body habitus of the target; comparingthe at least one bone structure parameter to a reference parameter toidentify a likely location of the fracture; and predicting the fractureload of the target with the target's bone structure parameter and acorrelation of the reference parameter.
 2. The method of claim 1,wherein the peak impact force is estimated.
 3. The method of claim 1,wherein the peak impact force is calculated.
 4. The method of claim 1,further comprising: generating bone parameter data corresponding to abone parameter map of at least a portion of the target.
 5. The method ofclaim 4, wherein the data is stored based on clinical risk factors. 6.The method of claim 4, further comprising storing the bone parameterdata in a database of bone parameter data.
 7. The method of claim 1,wherein the at least one bone structure parameter is at least oneparameter from the group consisting of an area ratio, a trabecularperimeter, and combinations thereof.
 8. The method of claim 7, whereinthe at least one bone structure parameter further comprises a first boneparameter being area ratio and a second bone parameter being trabecularperimeter.
 9. The method of claim 4, wherein the parameter map isderived using statistical comparisons of the bone structure parameter toa reference population.
 10. The method of claim 1, further comprisingidentifying local abnormalities of bone structure from the parametermap.
 11. The method of claim 1, further comprising: tracing low valuesor high values on the parameter map; and determining a potentialfracture line from the low values or high values.
 12. The method ofclaim 1, further comprising using watershed segmentation of parametermaps to identify the fracture line.
 13. The method of claim 1, whereinthe at least one bone structure parameter includes at least first andsecond bone structure parameters, and the method further comprisescalculating the fracture load from the first and second bone structureparameters.
 14. The method according to claim 1, wherein the bodyhabitus is related to a soft tissue thickness of the target.
 15. Themethod according to claim 1, wherein the body habitus is related to astanding height of the target.
 16. The method according to claim 1,wherein the body habitus is related to a body mass of the target. 17.The method of claim 1, wherein the bone structure parameter is a bonemicro-structure parameter.
 18. The method of claim 1, wherein the bonestructure parameter is a bone macro-structure parameter.
 19. The methodof claim 1, further comprising: transmitting the electronic image datato a second location; converting the electronic image data to a patternof normal or diseased using the bone structure parameter; and analyzingthe converted image.
 20. The method of claim 19, further comprisingtransmitting the pattern to a third location for analyzing.
 21. A systemfor analyzing musculoskeletal-related data of a target using a computer,comprising: means for receiving an image of a part of a skeleton of thetarget; means for deriving at least one bone structure parameter fromthe image; means for calculating a possibility of a fracture of thetarget using the at least one bone structure parameter of the target;means for analyzing the target's bone structure parameter in one or moreregions of interest (ROIs) to predict a possible fracture line andcalculate a fracture load of the target; means for estimating ormeasuring a body habitus of the target; and means for calculating a peakimpact force on the skeleton part when the target falls; means forobtaining a fracture load of a skeleton part of a reference; means forcorrelating a bone structure parameter of the reference to the fractureload of the reference; means for predicting a fracture risk bycalculating a ratio between the fracture load and the peak impact forceon the part of the skeleton of the target using a body habitus of thetarget; and means for receiving the target's bone structure parameterand the correlation of the reference's bone structure parameter, andcalculating the fracture load of the target.
 22. The system according toclaim 21 further comprising means for storing the correlation of thereference's bone structure parameter and the reference's fracture load.23. The system according to claim 22, wherein the means for storing isconfigured to receive clinical risk factors of the reference.